CARDAMOM-DALEC GPP Seasonality Drivers
1.1: all_pixels_compare_data_zero is a table of gpp,
lai, et, gsdsr, apar and cica extracted from every pixel across the
amazon and 5 regions over monthly time scale between 2001 and 2019
## gpp lai cica gsdsr apar
## Min. : 0.000 Min. :0.07593 Min. :0.05499 Min. :0.02047 Min. : 0.2157
## 1st Qu.: 5.819 1st Qu.:2.40496 1st Qu.:0.74130 1st Qu.:0.02832 1st Qu.: 5.2664
## Median : 8.854 Median :4.61883 Median :0.77583 Median :0.03551 Median : 6.9921
## Mean : 8.087 Mean :4.03754 Mean :0.74186 Mean :0.21638 Mean : 6.3363
## 3rd Qu.:10.603 3rd Qu.:5.74994 3rd Qu.:0.79912 3rd Qu.:0.26842 3rd Qu.: 7.7616
## Max. :16.258 Max. :6.40442 Max. :0.87102 Max. :1.00000 Max. :10.4986
##
## et region_name date month year
## Min. :0.000 amazon_nw: 42408 Min. :2001-01-01 Length:378252 Length:378252
## 1st Qu.:2.249 amazon_sw: 34656 1st Qu.:2005-09-23 Class :character Class :character
## Median :3.776 amazon_ec: 25536 Median :2010-06-16 Mode :character Mode :character
## Mean :3.266 amazon_bs: 98040 Mean :2010-06-16
## 3rd Qu.:4.304 amazon_gs: 31920 3rd Qu.:2015-03-08
## Max. :6.388 amazonia :145692 Max. :2019-12-01
##
## month_f
## Jan : 31521
## Feb : 31521
## Mar : 31521
## Apr : 31521
## May : 31521
## Jun : 31521
## (Other):189126## 'data.frame': 378252 obs. of 11 variables:
## $ gpp : num 7.89 6.31 5.47 5.22 6.6 ...
## $ lai : num 2.92 2.49 2.21 2.05 2.78 ...
## $ cica : num 0.806 0.766 0.66 0.647 0.632 ...
## $ gsdsr : num 0.246 1 1 1 1 ...
## $ apar : num 6.41 6.25 5.86 5.34 6.47 ...
## $ et : num 1.97 1.45 1.03 1.12 1.62 ...
## $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ date : Date, format: "2001-01-01" "2001-02-01" "2001-03-01" ...
## $ month : chr "Jan" "Feb" "Mar" "Apr" ...
## $ year : chr "2001" "2001" "2001" "2001" ...
## $ month_f : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 2 3 4 5 6 7 8 9 10 ...
## - attr(*, "na.action")= 'omit' Named int [1:787512] 1 2 3 4 5 6 7 8 9 10 ...
## ..- attr(*, "names")= chr [1:787512] "1" "2" "3" "4" ...## 'data.frame': 1368 obs. of 8 variables:
## $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 2 3 4 5 6 1 2 3 4 ...
## $ new_date : Factor w/ 228 levels "2001-01-01","2001-02-01",..: 1 1 1 1 1 1 2 2 2 2 ...
## $ gpp : num 8.31 6.62 10.07 6.52 10.19 ...
## $ lai : num 3.83 3.32 5.3 2.95 5.15 ...
## $ cica : num 0.759 0.743 0.742 0.746 0.747 ...
## $ gsdsr : num 0.222 0.338 0.036 0.382 0.144 ...
## $ apar : num 6.35 5.66 7.14 5.57 7.73 ...
## $ et : num 2.83 2.33 3.57 2.24 3.69 ...## [1] "Monthly Mean"## 'data.frame': 72 obs. of 8 variables:
## $ region_name: Factor w/ 6 levels "amazon_nw","amazon_sw",..: 1 2 3 4 5 6 1 2 3 4 ...
## $ month_f : Factor w/ 12 levels "Jan","Feb","Mar",..: 1 1 1 1 1 1 2 2 2 2 ...
## $ gpp : num 8.46 6.93 10.25 6.93 9.94 ...
## $ lai : num 3.85 3.32 5.24 2.9 5.07 ...
## $ cica : num 0.753 0.757 0.762 0.774 0.734 ...
## $ gsdsr : num 0.1637 0.2779 0.0355 0.3126 0.1709 ...
## $ apar : num 6.16 5.74 7.14 5.54 7.42 ...
## $ et : num 3.15 2.64 3.81 3.11 3.77 ...1.2: Time series plot of the spatial mean variables across the amazon
and 5 regions over monthly time scale between 2001 and 2019
1.3: Standardize data
1.4: Standardize all data
2.1: First, lm plots of various variable combinations for every region
over the time period
2.2: Second, lm plots of various variable combinations for every
region over the time period faceted monthly
